The present invention relates generally to the field of facial recognition, and more particularly to analysis by iterative synthesis.
A facial recognition system is a computer application for automatically identifying or verifying a person from a digital image or a video frame from a video source. One of the ways to do this is by comparing selected facial features from the image and a facial database. Such systems are typically used in security systems and can be compared to other biometric identification methods, such as fingerprint or eye iris recognition systems.
Some facial recognition algorithms identify facial features by extracting landmarks, or features, from an image of the subject's face. For example, an algorithm may analyze the relative position, size, and/or shape of the eyes, nose, cheekbones, and jaw. These features are then used to search for other images with matching features. Other algorithms normalize a gallery of face images and then compress the face data, only saving the data in the image that is useful for face recognition. A probe image is then compared with the compressed face data. One of the earliest successful systems was based on template matching techniques using principle component analysis of grayscale images, providing a type of compressed face representation. Recognition algorithms can be divided into two main approaches: geometric, which looks at discrete distinguishing features; and photometric, which is a statistical approach that extracts holistic descriptors from an image.
While there are known solutions for facial recognition and matching, there are often challenges that are associated with each of them. For example, 2D approaches are often confounded by changes in lighting or head orientation. In addition, they often do not work with low resolution images and cannot fuse information across multiple frames to improve themselves. While 3D approaches can better disentangle the effect of environment and true biometrics, and some can fuse information from multiple images, the approaches are unitary and cannot exploit additional information sources which may have been developed independently. Finally, most approaches perform identification by scoring all sample images in a gallery against the query face, which can take an inordinately long time as the database grows large.